# Load libraries, install if needed
pkgs <- c("tidyverse", "tidylog", "RCurl", "janitor", "terra", "viridis", "devtools", "ncdf4", "chron", "raster")
if(length(setdiff(pkgs, rownames(installed.packages()))) > 0){
install.packages(setdiff(pkgs, rownames(installed.packages())), dependencies = T)
}
invisible(lapply(pkgs, library, character.only = T))
# Source code for map plots
source_url("https://raw.githubusercontent.com/maxlindmark/cod-interactions/main/R/functions/map-plot.R")
home <- here::here()Summarise stomach content data
Load packages
Read data
d <- read_csv(paste0(home, "/data/clean/full_stomach_data.csv"))Summarize and organize data
We want 1 row = 1 predator and the total weight for each present prey type
# Calculate total weight of prey by predator ID and prey species (i.e., across prey sizes). First create wide data frame so that I can sum easily across prey groups (columns)
d_wide <- d %>%
group_by(pred_id, prey_latin_name) %>%
summarise(tot_prey_weight_g = sum(prey_weight_g)) %>%
ungroup() %>%
pivot_wider(names_from = prey_latin_name, values_from = tot_prey_weight_g) %>%
mutate_all(~ifelse(is.na(.), 0, .)) %>%
clean_names()
# Now make some calculations and aggregate to some taxonomic level. Since all columns are assigned to some higher level group (or the same group), the sum of these is the total stomach content. Note that I have one group for unidentified clupeids, but also sprat and herring. So if I want the total of some aggregated group, then I need to add all the sub-groups.
sort(colnames(d_wide %>% dplyr::select(-pred_id))) [1] "aglae" "agonus_cataphractus"
[3] "algae" "ammodytidae"
[5] "amphipoda" "aphia_minuta"
[7] "bivalvia" "bylgides_sarsi"
[9] "carbon" "carbon_2"
[11] "caridea" "clupea_harengus"
[13] "clupea_harengus_2" "clupeidae"
[15] "clupeidae_2" "copepoda"
[17] "crangon" "crangon_crangon"
[19] "crustacea" "cumacea"
[21] "decapoda" "diastylis_rathkei"
[23] "digestive_tract" "enchelyopus_cimbrius"
[25] "engraulis_encrasicolus" "gadus_morhua"
[27] "gammarus_sp" "gasterosteidae"
[29] "gasterosteus_aculeatus" "gastrosacus"
[31] "gobiidae" "gobiidae_2"
[33] "gobius_niger" "halicryptus"
[35] "halicryptus_spinolusus" "halicryptus_spinulosus"
[37] "halicryptus_spinulosus_2" "hediste_diversicolor"
[39] "hydrobia_sp" "hyperia_galba"
[41] "idotea_balthica" "idotea_sp"
[43] "limecola_balthica" "limecola_balthica_2"
[45] "litter_plastics" "macoma_balthica"
[47] "monoporeia_affinis" "mucus"
[49] "mucus_2" "mya_arenaria"
[51] "myoxocephalus_scorpius" "mysida"
[53] "mysidae" "mysis_mixta"
[55] "mytilus_edulis" "mytilus_sp"
[57] "mytilus_sp_2" "na"
[59] "neogobius_melanostomus" "neomysis_integer"
[61] "nephtys_ciliata" "palaemon_elegans"
[63] "palaemon_sp" "pectinaria_sp"
[65] "phyllodocida" "pisces"
[67] "pisces_2" "plastic"
[69] "plastics" "platichthys_flesus"
[71] "pleuronectidae" "polychaeta"
[73] "pontoporeia_femorata" "pontoporeia_femotara"
[75] "pontoporeidae" "pontoporeiidae"
[77] "prapulida" "praunus_flexuosus"
[79] "priapulida" "priapulida_2"
[81] "priapulus" "priapulus_caudatus"
[83] "priapulus_caudatus_2" "pungitius_pungitius"
[85] "remains" "remains_2"
[87] "saduria_entomon" "sand"
[89] "sand_2" "scales"
[91] "scales_2" "scoloplos_armiger"
[93] "scomber_scombrus" "sprattus_sprattus"
[95] "sprattus_sprattus_2" "stone"
[97] "stone_2" "waste"
[99] "wood" "wood_2"
[101] "zoarces_viviparus" d_wide2 <- d_wide %>%
mutate(amphipoda_tot = gammarus_sp + monoporeia_affinis + amphipoda + hyperia_galba,
bivalvia_tot = bivalvia + mytilus_sp + mytilus_sp_2 + mya_arenaria + macoma_balthica +
mytilus_edulis + limecola_balthica + limecola_balthica_2,
clupeidae_tot = clupeidae + clupeidae_2,
clupea_harengus_tot = clupea_harengus + clupea_harengus_2,
gadus_morhua_tot = gadus_morhua,
gobiidae_tot = gobiidae + gobiidae_2 + gobius_niger + neogobius_melanostomus + aphia_minuta,
mysidae_tot = mysidae + neomysis_integer + mysis_mixta + mysida + gastrosacus,
non_bio_tot = stone + stone_2 + plastic + plastics + sand + wood + carbon + carbon_2 + wood_2 +
litter_plastics + sand_2,
other_crustacea_tot = pontoporeia_femorata + pontoporeia_femotara + crangon +
crangon_crangon + idotea_balthica + cumacea + idotea_sp +
praunus_flexuosus + crustacea + diastylis_rathkei + palaemon_sp + palaemon_elegans + caridea +
copepoda + pontoporeiidae + decapoda + pontoporeidae,
other_tot = halicryptus_spinulosus + halicryptus_spinulosus_2 + priapulus + priapulus_caudatus + priapulus_caudatus_2 + algae + aglae +
waste + remains + remains_2 + hydrobia_sp +
priapulida + halicryptus + digestive_tract + mucus + mucus_2 +
halicryptus_spinolusus + priapulida_2 + prapulida,
other_pisces_tot = pisces + pisces_2 + enchelyopus_cimbrius + engraulis_encrasicolus +
gasterosteus_aculeatus + scales + scales_2 + scomber_scombrus +
pungitius_pungitius + zoarces_viviparus + ammodytidae +
pleuronectidae + gasterosteidae + agonus_cataphractus + myoxocephalus_scorpius,
platichthys_flesus_tot = platichthys_flesus,
polychaeta_tot = bylgides_sarsi + scoloplos_armiger + hediste_diversicolor +
phyllodocida + polychaeta + pectinaria_sp + nephtys_ciliata,
saduria_entomon_tot = saduria_entomon,
sprattus_sprattus_tot = sprattus_sprattus + sprattus_sprattus_2
)
# Check it's correct! The sum of all groups should be the sum of all categories
colnames(d_wide2 %>% dplyr::select(-pred_id)) [1] "diastylis_rathkei" "halicryptus_spinulosus"
[3] "bylgides_sarsi" "na"
[5] "clupeidae" "sprattus_sprattus"
[7] "mysis_mixta" "stone"
[9] "crangon_crangon" "gammarus_sp"
[11] "priapulida" "priapulus_caudatus"
[13] "gasterosteus_aculeatus" "pisces"
[15] "saduria_entomon" "neomysis_integer"
[17] "clupea_harengus" "monoporeia_affinis"
[19] "waste" "scales"
[21] "gobiidae" "pontoporeia_femorata"
[23] "remains" "crustacea"
[25] "mysidae" "limecola_balthica"
[27] "bivalvia" "halicryptus"
[29] "zoarces_viviparus" "platichthys_flesus"
[31] "digestive_tract" "wood"
[33] "phyllodocida" "polychaeta"
[35] "sand" "mytilus_sp"
[37] "amphipoda" "limecola_balthica_2"
[39] "algae" "pungitius_pungitius"
[41] "scoloplos_armiger" "gadus_morhua"
[43] "priapulida_2" "idotea_sp"
[45] "enchelyopus_cimbrius" "pleuronectidae"
[47] "cumacea" "plastic"
[49] "stone_2" "crangon"
[51] "sprattus_sprattus_2" "aglae"
[53] "macoma_balthica" "carbon"
[55] "gasterosteidae" "mysida"
[57] "gobiidae_2" "gobius_niger"
[59] "palaemon_sp" "mytilus_sp_2"
[61] "scales_2" "ammodytidae"
[63] "pectinaria_sp" "sand_2"
[65] "pontoporeiidae" "mucus"
[67] "pontoporeia_femotara" "remains_2"
[69] "mucus_2" "priapulus"
[71] "carbon_2" "wood_2"
[73] "halicryptus_spinulosus_2" "pisces_2"
[75] "mya_arenaria" "gastrosacus"
[77] "nephtys_ciliata" "litter_plastics"
[79] "clupeidae_2" "pontoporeidae"
[81] "decapoda" "praunus_flexuosus"
[83] "neogobius_melanostomus" "plastics"
[85] "agonus_cataphractus" "clupea_harengus_2"
[87] "copepoda" "halicryptus_spinolusus"
[89] "prapulida" "mytilus_edulis"
[91] "hydrobia_sp" "myoxocephalus_scorpius"
[93] "idotea_balthica" "caridea"
[95] "aphia_minuta" "hediste_diversicolor"
[97] "palaemon_elegans" "priapulus_caudatus_2"
[99] "scomber_scombrus" "hyperia_galba"
[101] "engraulis_encrasicolus" "amphipoda_tot"
[103] "bivalvia_tot" "clupeidae_tot"
[105] "clupea_harengus_tot" "gadus_morhua_tot"
[107] "gobiidae_tot" "mysidae_tot"
[109] "non_bio_tot" "other_crustacea_tot"
[111] "other_tot" "other_pisces_tot"
[113] "platichthys_flesus_tot" "polychaeta_tot"
[115] "saduria_entomon_tot" "sprattus_sprattus_tot" t <- d_wide2 %>%
dplyr::select(-pred_id) %>%
mutate(all_cat_sum = rowSums(.[1:100]),
group_sum = rowSums(.[101:115])) %>%
dplyr::select(all_cat_sum, group_sum) %>%
filter(!all_cat_sum == group_sum)
# Must be some rounding error...
d_wide2 %>%
dplyr::select(-pred_id) %>%
mutate(all_cat_sum = rowSums(.[1:100]),
group_sum = rowSums(.[101:115])) %>%
dplyr::select(all_cat_sum, group_sum) %>%
ggplot(aes(all_cat_sum, group_sum)) +
geom_point() +
geom_abline(color = "red")
# Select only columns aggregated columns (ending with _tot) (all columns (prey) are represented there)
colnames(d_wide2) [1] "pred_id" "diastylis_rathkei"
[3] "halicryptus_spinulosus" "bylgides_sarsi"
[5] "na" "clupeidae"
[7] "sprattus_sprattus" "mysis_mixta"
[9] "stone" "crangon_crangon"
[11] "gammarus_sp" "priapulida"
[13] "priapulus_caudatus" "gasterosteus_aculeatus"
[15] "pisces" "saduria_entomon"
[17] "neomysis_integer" "clupea_harengus"
[19] "monoporeia_affinis" "waste"
[21] "scales" "gobiidae"
[23] "pontoporeia_femorata" "remains"
[25] "crustacea" "mysidae"
[27] "limecola_balthica" "bivalvia"
[29] "halicryptus" "zoarces_viviparus"
[31] "platichthys_flesus" "digestive_tract"
[33] "wood" "phyllodocida"
[35] "polychaeta" "sand"
[37] "mytilus_sp" "amphipoda"
[39] "limecola_balthica_2" "algae"
[41] "pungitius_pungitius" "scoloplos_armiger"
[43] "gadus_morhua" "priapulida_2"
[45] "idotea_sp" "enchelyopus_cimbrius"
[47] "pleuronectidae" "cumacea"
[49] "plastic" "stone_2"
[51] "crangon" "sprattus_sprattus_2"
[53] "aglae" "macoma_balthica"
[55] "carbon" "gasterosteidae"
[57] "mysida" "gobiidae_2"
[59] "gobius_niger" "palaemon_sp"
[61] "mytilus_sp_2" "scales_2"
[63] "ammodytidae" "pectinaria_sp"
[65] "sand_2" "pontoporeiidae"
[67] "mucus" "pontoporeia_femotara"
[69] "remains_2" "mucus_2"
[71] "priapulus" "carbon_2"
[73] "wood_2" "halicryptus_spinulosus_2"
[75] "pisces_2" "mya_arenaria"
[77] "gastrosacus" "nephtys_ciliata"
[79] "litter_plastics" "clupeidae_2"
[81] "pontoporeidae" "decapoda"
[83] "praunus_flexuosus" "neogobius_melanostomus"
[85] "plastics" "agonus_cataphractus"
[87] "clupea_harengus_2" "copepoda"
[89] "halicryptus_spinolusus" "prapulida"
[91] "mytilus_edulis" "hydrobia_sp"
[93] "myoxocephalus_scorpius" "idotea_balthica"
[95] "caridea" "aphia_minuta"
[97] "hediste_diversicolor" "palaemon_elegans"
[99] "priapulus_caudatus_2" "scomber_scombrus"
[101] "hyperia_galba" "engraulis_encrasicolus"
[103] "amphipoda_tot" "bivalvia_tot"
[105] "clupeidae_tot" "clupea_harengus_tot"
[107] "gadus_morhua_tot" "gobiidae_tot"
[109] "mysidae_tot" "non_bio_tot"
[111] "other_crustacea_tot" "other_tot"
[113] "other_pisces_tot" "platichthys_flesus_tot"
[115] "polychaeta_tot" "saduria_entomon_tot"
[117] "sprattus_sprattus_tot" d_wide3 <- d_wide2 %>%
dplyr::select(pred_id, ends_with("_tot"))
# Add back in other information about the predator ID
d_sel <- d %>%
dplyr::select(predator_latin_name, species, pred_weight_g, pred_length_cm,
year, quarter, month, ices_rect, subdiv, haul_id, smhi_serial_no,
X, Y, lat, lon, pred_id, fle_kg_km2, mcod_kg_km2, scod_kg_km2, date,
bottom_depth
) %>%
distinct(pred_id, .keep_all = TRUE)
d_wide3 <- left_join(d_wide3, d_sel)
# mutate(size_group = ifelse(lengthcl >= 100 & lengthcl < 250, "small", NA),
# size_group = ifelse(lengthcl >= 250 & lengthcl < 500, "medium", size_group)) %>%
d_wide3 <- d_wide3 %>%
mutate(group = NA,
group = ifelse(species == "Flounder", "flounder", group),
group = ifelse(species == "Cod" & pred_length_cm >= 10 & pred_length_cm <= 25, "small cod", group),
group = ifelse(species == "Cod" & pred_length_cm > 25 & pred_length_cm <= 50, "large cod", group))Add covariates
Depth
dep_raster <- terra::rast(paste0(home, "/data/Mean depth natural colour (with land).nc"))
class(dep_raster)[1] "SpatRaster"
attr(,"package")
[1] "terra"crs(dep_raster, proj = TRUE)[1] "+proj=longlat +datum=WGS84 +no_defs"plot(dep_raster)
d_wide3$depth <- terra::extract(dep_raster, d_wide3 %>% dplyr::select(lon, lat))$elevation
ggplot(d_wide3, aes(lon, lat, color = depth*-1)) +
geom_point()
d_wide3$depth <- d_wide3$depth*-1
d_wide3 <- d_wide3 |> drop_na(depth)drop_na: no rows removedplot_map +
geom_point(data = d_wide3, aes(X*1000, Y*1000, color = depth)) +
scale_color_viridis()
ggplot(d_wide3, aes(depth, bottom_depth)) +
geom_point() +
geom_abline()
# So strange. What does it look like in the normal data?
t <- d_wide3 %>%
filter(bottom_depth > 250)filter: removed 5,396 rows (56%), 4,253 rows remainingggplot(t, aes(depth, bottom_depth/10)) +
geom_point() +
geom_abline()
strange_dept <- d %>% filter(haul_id %in% c(t$haul_id))filter: removed 49,130 rows (54%), 42,145 rows remainingstrange_dept %>%
summary() pred_id data_id gall_bladder stomach_state
Length:42145 Length:42145 Length:42145 Length:42145
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
predator_code prey_weight_g stage_digestion comment
Length:42145 Min. : 0.00000 Min. :0.000 Length:42145
Class :character 1st Qu.: 0.01069 1st Qu.:1.000 Class :character
Mode :character Median : 0.02562 Median :1.000 Mode :character
Mean : 0.31695 Mean :1.063
3rd Qu.: 0.06588 3rd Qu.:1.000
Max. :144.34000 Max. :2.000
NA's :1118
prey_length_cm prey_latin_name length_code n_empty
Min. : 4.00 Length:42145 Length:42145 Min. :0.000
1st Qu.: 20.00 Class :character Class :character 1st Qu.:0.000
Median : 30.00 Mode :character Mode :character Median :0.000
Mean : 45.83 Mean :0.031
3rd Qu.: 48.00 3rd Qu.:0.000
Max. :300.00 Max. :1.000
NA's :39172 NA's :10698
n_with_food n_regurgitated haul_id fle_kg_km2
Min. :0.0000 Min. :0.000 Length:42145 Min. : 0.0
1st Qu.:1.0000 1st Qu.:0.000 Class :character 1st Qu.: 540.5
Median :1.0000 Median :0.000 Mode :character Median : 1137.2
Mean :0.9761 Mean :0.003 Mean : 1740.3
3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.: 1961.9
Max. :1.0000 Max. :1.000 Max. :18668.7
NA's :313 NA's :10911
mcod_kg_km2 scod_kg_km2 year quarter
Min. : 0.0 Min. : 0.0 Min. :2015 Min. :1.000
1st Qu.: 149.6 1st Qu.: 102.0 1st Qu.:2017 1st Qu.:1.000
Median : 1287.8 Median : 462.2 Median :2021 Median :4.000
Mean : 4724.4 Mean : 1771.7 Mean :2020 Mean :3.184
3rd Qu.: 4007.8 3rd Qu.: 2012.0 3rd Qu.:2022 3rd Qu.:4.000
Max. :171072.0 Max. :41057.3 Max. :2022 Max. :4.000
lat lon bottom_depth species
Min. :55.17 Min. :13.30 Min. : 333.0 Length:42145
1st Qu.:55.67 1st Qu.:14.47 1st Qu.: 418.0 Class :character
Median :55.82 Median :15.80 Median : 473.0 Mode :character
Mean :56.24 Mean :16.31 Mean : 490.7
3rd Qu.:57.15 3rd Qu.:18.40 3rd Qu.: 552.0
Max. :58.45 Max. :19.52 Max. :1216.0
pred_length_cm pred_weight_g size subdiv
Min. : 4.00 Min. : 1.0 Min. :9 Min. :24.00
1st Qu.:23.00 1st Qu.: 120.0 1st Qu.:9 1st Qu.:25.00
Median :26.00 Median : 185.0 Median :9 Median :25.00
Mean :26.66 Mean : 224.9 Mean :9 Mean :25.74
3rd Qu.:31.00 3rd Qu.: 292.0 3rd Qu.:9 3rd Qu.:27.00
Max. :77.00 Max. :4798.0 Max. :9 Max. :28.00
date month X Y
Min. :2015-11-20 Min. : 2.000 Min. :391.7 Min. :6115
1st Qu.:2017-02-26 1st Qu.: 3.000 1st Qu.:466.5 1st Qu.:6169
Median :2021-11-21 Median :11.000 Median :550.2 Median :6186
Mean :2020-05-24 Mean : 8.588 Mean :578.5 Mean :6236
3rd Qu.:2022-02-26 3rd Qu.:11.000 3rd Qu.:710.3 3rd Qu.:6341
Max. :2022-11-27 Max. :11.000 Max. :768.0 Max. :6487
smhi_serial_no prey_number_type prey_weight_type ices_rect
Min. : 1.0 Length:42145 Length:42145 Length:42145
1st Qu.: 17.0 Class :character Class :character Class :character
Median : 201.0 Mode :character Mode :character Mode :character
Mean : 442.2
3rd Qu.: 969.0
Max. :1014.0
NA's :13421
predator_latin_name rel_weight
Length:42145 Min. :0.000e+00
Class :character 1st Qu.:6.357e-05
Mode :character Median :1.657e-04
Mean :9.743e-04
3rd Qu.:4.557e-04
Max. :8.300e-02
unique(strange_dept$quarter)[1] 4 1unique(strange_dept$year)[1] 2015 2016 2017 2019 2020 2021 2022unique(strange_dept$month)[1] 11 2 3unique(strange_dept$subdiv)[1] 25 26 28 24 27Add in environmental variables
dat <- d_wide3 %>%
dplyr::select(haul_id, X, Y, lon, lat, year, month, quarter) %>%
distinct(haul_id, .keep_all = TRUE) %>%
mutate(#month = ifelse(quarter == 1, 2, 11), # most common months: only for prediction grid
month_year = paste(month, year, sep = "_"))distinct: removed 9,295 rows (96%), 354 rows remainingmutate: new variable 'month_year' (character) with 20 unique values and 0% NAdat %>% head()# A tibble: 6 × 9
haul_id X Y lon lat year month quarter month_year
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 2015_4_2 475. 6165. 14.6 55.6 2015 11 4 11_2015
2 2015_4_4 469. 6171. 14.5 55.7 2015 11 4 11_2015
3 2015_4_6 460. 6173. 14.4 55.7 2015 11 4 11_2015
4 2015_4_8 647. 6216. 17.4 56.1 2015 11 4 11_2015
5 2015_4_9 710. 6247. 18.4 56.3 2015 11 4 11_2015
6 2015_4_12 722. 6255. 18.6 56.4 2015 11 4 11_2015 # For temperature and oxygen, we need to use two different rasters because we don't have the updated data in the re-analyisis
# TODO: check this later
dat_1 <- dat %>% filter(year < 2022)filter: removed 49 rows (14%), 305 rows remainingdat_2 <- dat %>% filter(year >= 2022)filter: removed 305 rows (86%), 49 rows remainingSaduria
saduria <- terra::rast(paste0(home, "/data/saduria-tif/FWBiomassm_raster_19812019presHighweightcor_no0_newZi.tif"))
WGS84 <- "+proj=longlat +datum=WGS84"
saduria_latlon <- terra::project(saduria, WGS84)
density_saduria <- terra::extract(saduria_latlon, dat %>% dplyr::select(lon, lat))
dat$density_saduria <- density_saduria$FWBiomassm_raster_19812019presHighweightcor_no0_newZi
plot_map +
geom_point(data = dat, aes(X*1000, Y*1000, color = density_saduria)) +
scale_color_viridis(trans = "sqrt", name = "Saduria biomass density")
Oxygen
# Downloaded from here: https://data.marine.copernicus.eu/products
# Extract raster points: https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
# https://rpubs.com/boyerag/297592
# https://pjbartlein.github.io/REarthSysSci/netCDF.html#get-a-variable
# Open the netCDF file
ncin <- nc_open(paste0(home, "/data/NEMO/cmems_mod_bal_bgc_my_P1D-m_1702039771395.nc"))
print(ncin)File /Users/maxlindmark/Dropbox/Max work/R/cod-interactions/data/NEMO/cmems_mod_bal_bgc_my_P1D-m_1702039771395.nc (NC_FORMAT_CLASSIC):
1 variables (excluding dimension variables):
float o2b[lon,lat,time]
standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
long_name: Dissolved_Oxygen_Concentration
units: mmol m-3
_FillValue: -999
missing_value: -999
unit_long: millimole O2 per unit volume
interval_write: 1 d
interval_operation: 90 s
online_operation: average
_ChunkSizes: 1
_ChunkSizes: 774
_ChunkSizes: 763
3 dimensions:
time Size:2557
standard_name: time
long_name: time
units: days since 1900-01-01 00:00:00
calendar: standard
axis: T
_ChunkSizes: 512
_CoordinateAxisType: Time
valid_min: 42003.5
valid_max: 44559.5
lat Size:289
standard_name: latitude
long_name: Latitude of each location
units: degrees_north
axis: Y
_CoordinateAxisType: Lat
valid_min: 53.2582931518555
valid_max: 58.0582122802734
lon Size:336
standard_name: longitude
long_name: Longitude of each location
units: degrees_east
axis: X
_CoordinateAxisType: Lon
valid_min: 12.5414085388184
valid_max: 21.8466091156006
22 global attributes:
CDI: Climate Data Interface version 1.9.9rc1 (https://mpimet.mpg.de/cdi)
Conventions: CF-1.0
history: Tue Mar 14 17:24:24 2023: cdo -z zip_1 --sortname copy /net/isilon/ifs/arch/home/iri/BALMFC/Nemo4.0/BALMFCMYP2022_univariateSST//BAL-ERGOM_BGC-DailyMeans-20211231.nc tmp_20211231.nc
source: CMEMS BAL MFC NEMO model output converted to NetCDF
institution: Baltic MFC, PU Danish Meteorological Institute
comment: Data on cropped native product grid. Horizontal velocities destagged
grid_resolution: ~1 nautical mile (1min latitude; 1min40sec longitude)
title: CMEMS ERGOM daily integrated model fields
contact: servicedesk.cmems@mercator-ocean.eu
references: https://marine.copernicus.eu/
file_quality_index: 1
compression: yes
stop_date: 2021-12-31 12:00:00
creation_date: 2023-03-14 17:24:23
FROM_ORIGINAL_FILE__netcdf_version_id: 4.6.1 of Oct 19 2018 20:03:53 $
FROM_ORIGINAL_FILE__easternmost_longitude: 30.2074012756348
FROM_ORIGINAL_FILE__northernmost_latitude: 65.8909912109375
FROM_ORIGINAL_FILE__westernmost_longitude: 9.04154205322266
FROM_ORIGINAL_FILE__southernmost_latitude: 53.0082969665527
start_date: 2021-12-31 12:00:00
CDO: Climate Data Operators version 1.9.9rc1 (https://mpimet.mpg.de/cdo)
_CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention# Get longitude and latitude
lon <- ncvar_get(ncin,"lon")
nlon <- dim(lon)
head(lon)[1] 12.54141 12.56919 12.59696 12.62474 12.65252 12.68029lat <- ncvar_get(ncin,"lat")
nlat <- dim(lat)
head(lat)[1] 53.25829 53.27496 53.29163 53.30829 53.32496 53.34163# Get time
time <- ncvar_get(ncin,"time")
time [1] 42003.5 42004.5 42005.5 42006.5 42007.5 42008.5 42009.5 42010.5 42011.5
[10] 42012.5 42013.5 42014.5 42015.5 42016.5 42017.5 42018.5 42019.5 42020.5
[19] 42021.5 42022.5 42023.5 42024.5 42025.5 42026.5 42027.5 42028.5 42029.5
[28] 42030.5 42031.5 42032.5 42033.5 42034.5 42035.5 42036.5 42037.5 42038.5
[37] 42039.5 42040.5 42041.5 42042.5 42043.5 42044.5 42045.5 42046.5 42047.5
[46] 42048.5 42049.5 42050.5 42051.5 42052.5 42053.5 42054.5 42055.5 42056.5
[55] 42057.5 42058.5 42059.5 42060.5 42061.5 42062.5 42063.5 42064.5 42065.5
[64] 42066.5 42067.5 42068.5 42069.5 42070.5 42071.5 42072.5 42073.5 42074.5
[73] 42075.5 42076.5 42077.5 42078.5 42079.5 42080.5 42081.5 42082.5 42083.5
[82] 42084.5 42085.5 42086.5 42087.5 42088.5 42089.5 42090.5 42091.5 42092.5
[91] 42093.5 42094.5 42095.5 42096.5 42097.5 42098.5 42099.5 42100.5 42101.5
[100] 42102.5 42103.5 42104.5 42105.5 42106.5 42107.5 42108.5 42109.5 42110.5
[109] 42111.5 42112.5 42113.5 42114.5 42115.5 42116.5 42117.5 42118.5 42119.5
[118] 42120.5 42121.5 42122.5 42123.5 42124.5 42125.5 42126.5 42127.5 42128.5
[127] 42129.5 42130.5 42131.5 42132.5 42133.5 42134.5 42135.5 42136.5 42137.5
[136] 42138.5 42139.5 42140.5 42141.5 42142.5 42143.5 42144.5 42145.5 42146.5
[145] 42147.5 42148.5 42149.5 42150.5 42151.5 42152.5 42153.5 42154.5 42155.5
[154] 42156.5 42157.5 42158.5 42159.5 42160.5 42161.5 42162.5 42163.5 42164.5
[163] 42165.5 42166.5 42167.5 42168.5 42169.5 42170.5 42171.5 42172.5 42173.5
[172] 42174.5 42175.5 42176.5 42177.5 42178.5 42179.5 42180.5 42181.5 42182.5
[181] 42183.5 42184.5 42185.5 42186.5 42187.5 42188.5 42189.5 42190.5 42191.5
[190] 42192.5 42193.5 42194.5 42195.5 42196.5 42197.5 42198.5 42199.5 42200.5
[199] 42201.5 42202.5 42203.5 42204.5 42205.5 42206.5 42207.5 42208.5 42209.5
[208] 42210.5 42211.5 42212.5 42213.5 42214.5 42215.5 42216.5 42217.5 42218.5
[217] 42219.5 42220.5 42221.5 42222.5 42223.5 42224.5 42225.5 42226.5 42227.5
[226] 42228.5 42229.5 42230.5 42231.5 42232.5 42233.5 42234.5 42235.5 42236.5
[235] 42237.5 42238.5 42239.5 42240.5 42241.5 42242.5 42243.5 42244.5 42245.5
[244] 42246.5 42247.5 42248.5 42249.5 42250.5 42251.5 42252.5 42253.5 42254.5
[253] 42255.5 42256.5 42257.5 42258.5 42259.5 42260.5 42261.5 42262.5 42263.5
[262] 42264.5 42265.5 42266.5 42267.5 42268.5 42269.5 42270.5 42271.5 42272.5
[271] 42273.5 42274.5 42275.5 42276.5 42277.5 42278.5 42279.5 42280.5 42281.5
[280] 42282.5 42283.5 42284.5 42285.5 42286.5 42287.5 42288.5 42289.5 42290.5
[289] 42291.5 42292.5 42293.5 42294.5 42295.5 42296.5 42297.5 42298.5 42299.5
[298] 42300.5 42301.5 42302.5 42303.5 42304.5 42305.5 42306.5 42307.5 42308.5
[307] 42309.5 42310.5 42311.5 42312.5 42313.5 42314.5 42315.5 42316.5 42317.5
[316] 42318.5 42319.5 42320.5 42321.5 42322.5 42323.5 42324.5 42325.5 42326.5
[325] 42327.5 42328.5 42329.5 42330.5 42331.5 42332.5 42333.5 42334.5 42335.5
[334] 42336.5 42337.5 42338.5 42339.5 42340.5 42341.5 42342.5 42343.5 42344.5
[343] 42345.5 42346.5 42347.5 42348.5 42349.5 42350.5 42351.5 42352.5 42353.5
[352] 42354.5 42355.5 42356.5 42357.5 42358.5 42359.5 42360.5 42361.5 42362.5
[361] 42363.5 42364.5 42365.5 42366.5 42367.5 42368.5 42369.5 42370.5 42371.5
[370] 42372.5 42373.5 42374.5 42375.5 42376.5 42377.5 42378.5 42379.5 42380.5
[379] 42381.5 42382.5 42383.5 42384.5 42385.5 42386.5 42387.5 42388.5 42389.5
[388] 42390.5 42391.5 42392.5 42393.5 42394.5 42395.5 42396.5 42397.5 42398.5
[397] 42399.5 42400.5 42401.5 42402.5 42403.5 42404.5 42405.5 42406.5 42407.5
[406] 42408.5 42409.5 42410.5 42411.5 42412.5 42413.5 42414.5 42415.5 42416.5
[415] 42417.5 42418.5 42419.5 42420.5 42421.5 42422.5 42423.5 42424.5 42425.5
[424] 42426.5 42427.5 42428.5 42429.5 42430.5 42431.5 42432.5 42433.5 42434.5
[433] 42435.5 42436.5 42437.5 42438.5 42439.5 42440.5 42441.5 42442.5 42443.5
[442] 42444.5 42445.5 42446.5 42447.5 42448.5 42449.5 42450.5 42451.5 42452.5
[451] 42453.5 42454.5 42455.5 42456.5 42457.5 42458.5 42459.5 42460.5 42461.5
[460] 42462.5 42463.5 42464.5 42465.5 42466.5 42467.5 42468.5 42469.5 42470.5
[469] 42471.5 42472.5 42473.5 42474.5 42475.5 42476.5 42477.5 42478.5 42479.5
[478] 42480.5 42481.5 42482.5 42483.5 42484.5 42485.5 42486.5 42487.5 42488.5
[487] 42489.5 42490.5 42491.5 42492.5 42493.5 42494.5 42495.5 42496.5 42497.5
[496] 42498.5 42499.5 42500.5 42501.5 42502.5 42503.5 42504.5 42505.5 42506.5
[505] 42507.5 42508.5 42509.5 42510.5 42511.5 42512.5 42513.5 42514.5 42515.5
[514] 42516.5 42517.5 42518.5 42519.5 42520.5 42521.5 42522.5 42523.5 42524.5
[523] 42525.5 42526.5 42527.5 42528.5 42529.5 42530.5 42531.5 42532.5 42533.5
[532] 42534.5 42535.5 42536.5 42537.5 42538.5 42539.5 42540.5 42541.5 42542.5
[541] 42543.5 42544.5 42545.5 42546.5 42547.5 42548.5 42549.5 42550.5 42551.5
[550] 42552.5 42553.5 42554.5 42555.5 42556.5 42557.5 42558.5 42559.5 42560.5
[559] 42561.5 42562.5 42563.5 42564.5 42565.5 42566.5 42567.5 42568.5 42569.5
[568] 42570.5 42571.5 42572.5 42573.5 42574.5 42575.5 42576.5 42577.5 42578.5
[577] 42579.5 42580.5 42581.5 42582.5 42583.5 42584.5 42585.5 42586.5 42587.5
[586] 42588.5 42589.5 42590.5 42591.5 42592.5 42593.5 42594.5 42595.5 42596.5
[595] 42597.5 42598.5 42599.5 42600.5 42601.5 42602.5 42603.5 42604.5 42605.5
[604] 42606.5 42607.5 42608.5 42609.5 42610.5 42611.5 42612.5 42613.5 42614.5
[613] 42615.5 42616.5 42617.5 42618.5 42619.5 42620.5 42621.5 42622.5 42623.5
[622] 42624.5 42625.5 42626.5 42627.5 42628.5 42629.5 42630.5 42631.5 42632.5
[631] 42633.5 42634.5 42635.5 42636.5 42637.5 42638.5 42639.5 42640.5 42641.5
[640] 42642.5 42643.5 42644.5 42645.5 42646.5 42647.5 42648.5 42649.5 42650.5
[649] 42651.5 42652.5 42653.5 42654.5 42655.5 42656.5 42657.5 42658.5 42659.5
[658] 42660.5 42661.5 42662.5 42663.5 42664.5 42665.5 42666.5 42667.5 42668.5
[667] 42669.5 42670.5 42671.5 42672.5 42673.5 42674.5 42675.5 42676.5 42677.5
[676] 42678.5 42679.5 42680.5 42681.5 42682.5 42683.5 42684.5 42685.5 42686.5
[685] 42687.5 42688.5 42689.5 42690.5 42691.5 42692.5 42693.5 42694.5 42695.5
[694] 42696.5 42697.5 42698.5 42699.5 42700.5 42701.5 42702.5 42703.5 42704.5
[703] 42705.5 42706.5 42707.5 42708.5 42709.5 42710.5 42711.5 42712.5 42713.5
[712] 42714.5 42715.5 42716.5 42717.5 42718.5 42719.5 42720.5 42721.5 42722.5
[721] 42723.5 42724.5 42725.5 42726.5 42727.5 42728.5 42729.5 42730.5 42731.5
[730] 42732.5 42733.5 42734.5 42735.5 42736.5 42737.5 42738.5 42739.5 42740.5
[739] 42741.5 42742.5 42743.5 42744.5 42745.5 42746.5 42747.5 42748.5 42749.5
[748] 42750.5 42751.5 42752.5 42753.5 42754.5 42755.5 42756.5 42757.5 42758.5
[757] 42759.5 42760.5 42761.5 42762.5 42763.5 42764.5 42765.5 42766.5 42767.5
[766] 42768.5 42769.5 42770.5 42771.5 42772.5 42773.5 42774.5 42775.5 42776.5
[775] 42777.5 42778.5 42779.5 42780.5 42781.5 42782.5 42783.5 42784.5 42785.5
[784] 42786.5 42787.5 42788.5 42789.5 42790.5 42791.5 42792.5 42793.5 42794.5
[793] 42795.5 42796.5 42797.5 42798.5 42799.5 42800.5 42801.5 42802.5 42803.5
[802] 42804.5 42805.5 42806.5 42807.5 42808.5 42809.5 42810.5 42811.5 42812.5
[811] 42813.5 42814.5 42815.5 42816.5 42817.5 42818.5 42819.5 42820.5 42821.5
[820] 42822.5 42823.5 42824.5 42825.5 42826.5 42827.5 42828.5 42829.5 42830.5
[829] 42831.5 42832.5 42833.5 42834.5 42835.5 42836.5 42837.5 42838.5 42839.5
[838] 42840.5 42841.5 42842.5 42843.5 42844.5 42845.5 42846.5 42847.5 42848.5
[847] 42849.5 42850.5 42851.5 42852.5 42853.5 42854.5 42855.5 42856.5 42857.5
[856] 42858.5 42859.5 42860.5 42861.5 42862.5 42863.5 42864.5 42865.5 42866.5
[865] 42867.5 42868.5 42869.5 42870.5 42871.5 42872.5 42873.5 42874.5 42875.5
[874] 42876.5 42877.5 42878.5 42879.5 42880.5 42881.5 42882.5 42883.5 42884.5
[883] 42885.5 42886.5 42887.5 42888.5 42889.5 42890.5 42891.5 42892.5 42893.5
[892] 42894.5 42895.5 42896.5 42897.5 42898.5 42899.5 42900.5 42901.5 42902.5
[901] 42903.5 42904.5 42905.5 42906.5 42907.5 42908.5 42909.5 42910.5 42911.5
[910] 42912.5 42913.5 42914.5 42915.5 42916.5 42917.5 42918.5 42919.5 42920.5
[919] 42921.5 42922.5 42923.5 42924.5 42925.5 42926.5 42927.5 42928.5 42929.5
[928] 42930.5 42931.5 42932.5 42933.5 42934.5 42935.5 42936.5 42937.5 42938.5
[937] 42939.5 42940.5 42941.5 42942.5 42943.5 42944.5 42945.5 42946.5 42947.5
[946] 42948.5 42949.5 42950.5 42951.5 42952.5 42953.5 42954.5 42955.5 42956.5
[955] 42957.5 42958.5 42959.5 42960.5 42961.5 42962.5 42963.5 42964.5 42965.5
[964] 42966.5 42967.5 42968.5 42969.5 42970.5 42971.5 42972.5 42973.5 42974.5
[973] 42975.5 42976.5 42977.5 42978.5 42979.5 42980.5 42981.5 42982.5 42983.5
[982] 42984.5 42985.5 42986.5 42987.5 42988.5 42989.5 42990.5 42991.5 42992.5
[991] 42993.5 42994.5 42995.5 42996.5 42997.5 42998.5 42999.5 43000.5 43001.5
[1000] 43002.5 43003.5 43004.5 43005.5 43006.5 43007.5 43008.5 43009.5 43010.5
[1009] 43011.5 43012.5 43013.5 43014.5 43015.5 43016.5 43017.5 43018.5 43019.5
[1018] 43020.5 43021.5 43022.5 43023.5 43024.5 43025.5 43026.5 43027.5 43028.5
[1027] 43029.5 43030.5 43031.5 43032.5 43033.5 43034.5 43035.5 43036.5 43037.5
[1036] 43038.5 43039.5 43040.5 43041.5 43042.5 43043.5 43044.5 43045.5 43046.5
[1045] 43047.5 43048.5 43049.5 43050.5 43051.5 43052.5 43053.5 43054.5 43055.5
[1054] 43056.5 43057.5 43058.5 43059.5 43060.5 43061.5 43062.5 43063.5 43064.5
[1063] 43065.5 43066.5 43067.5 43068.5 43069.5 43070.5 43071.5 43072.5 43073.5
[1072] 43074.5 43075.5 43076.5 43077.5 43078.5 43079.5 43080.5 43081.5 43082.5
[1081] 43083.5 43084.5 43085.5 43086.5 43087.5 43088.5 43089.5 43090.5 43091.5
[1090] 43092.5 43093.5 43094.5 43095.5 43096.5 43097.5 43098.5 43099.5 43100.5
[1099] 43101.5 43102.5 43103.5 43104.5 43105.5 43106.5 43107.5 43108.5 43109.5
[1108] 43110.5 43111.5 43112.5 43113.5 43114.5 43115.5 43116.5 43117.5 43118.5
[1117] 43119.5 43120.5 43121.5 43122.5 43123.5 43124.5 43125.5 43126.5 43127.5
[1126] 43128.5 43129.5 43130.5 43131.5 43132.5 43133.5 43134.5 43135.5 43136.5
[1135] 43137.5 43138.5 43139.5 43140.5 43141.5 43142.5 43143.5 43144.5 43145.5
[1144] 43146.5 43147.5 43148.5 43149.5 43150.5 43151.5 43152.5 43153.5 43154.5
[1153] 43155.5 43156.5 43157.5 43158.5 43159.5 43160.5 43161.5 43162.5 43163.5
[1162] 43164.5 43165.5 43166.5 43167.5 43168.5 43169.5 43170.5 43171.5 43172.5
[1171] 43173.5 43174.5 43175.5 43176.5 43177.5 43178.5 43179.5 43180.5 43181.5
[1180] 43182.5 43183.5 43184.5 43185.5 43186.5 43187.5 43188.5 43189.5 43190.5
[1189] 43191.5 43192.5 43193.5 43194.5 43195.5 43196.5 43197.5 43198.5 43199.5
[1198] 43200.5 43201.5 43202.5 43203.5 43204.5 43205.5 43206.5 43207.5 43208.5
[1207] 43209.5 43210.5 43211.5 43212.5 43213.5 43214.5 43215.5 43216.5 43217.5
[1216] 43218.5 43219.5 43220.5 43221.5 43222.5 43223.5 43224.5 43225.5 43226.5
[1225] 43227.5 43228.5 43229.5 43230.5 43231.5 43232.5 43233.5 43234.5 43235.5
[1234] 43236.5 43237.5 43238.5 43239.5 43240.5 43241.5 43242.5 43243.5 43244.5
[1243] 43245.5 43246.5 43247.5 43248.5 43249.5 43250.5 43251.5 43252.5 43253.5
[1252] 43254.5 43255.5 43256.5 43257.5 43258.5 43259.5 43260.5 43261.5 43262.5
[1261] 43263.5 43264.5 43265.5 43266.5 43267.5 43268.5 43269.5 43270.5 43271.5
[1270] 43272.5 43273.5 43274.5 43275.5 43276.5 43277.5 43278.5 43279.5 43280.5
[1279] 43281.5 43282.5 43283.5 43284.5 43285.5 43286.5 43287.5 43288.5 43289.5
[1288] 43290.5 43291.5 43292.5 43293.5 43294.5 43295.5 43296.5 43297.5 43298.5
[1297] 43299.5 43300.5 43301.5 43302.5 43303.5 43304.5 43305.5 43306.5 43307.5
[1306] 43308.5 43309.5 43310.5 43311.5 43312.5 43313.5 43314.5 43315.5 43316.5
[1315] 43317.5 43318.5 43319.5 43320.5 43321.5 43322.5 43323.5 43324.5 43325.5
[1324] 43326.5 43327.5 43328.5 43329.5 43330.5 43331.5 43332.5 43333.5 43334.5
[1333] 43335.5 43336.5 43337.5 43338.5 43339.5 43340.5 43341.5 43342.5 43343.5
[1342] 43344.5 43345.5 43346.5 43347.5 43348.5 43349.5 43350.5 43351.5 43352.5
[1351] 43353.5 43354.5 43355.5 43356.5 43357.5 43358.5 43359.5 43360.5 43361.5
[1360] 43362.5 43363.5 43364.5 43365.5 43366.5 43367.5 43368.5 43369.5 43370.5
[1369] 43371.5 43372.5 43373.5 43374.5 43375.5 43376.5 43377.5 43378.5 43379.5
[1378] 43380.5 43381.5 43382.5 43383.5 43384.5 43385.5 43386.5 43387.5 43388.5
[1387] 43389.5 43390.5 43391.5 43392.5 43393.5 43394.5 43395.5 43396.5 43397.5
[1396] 43398.5 43399.5 43400.5 43401.5 43402.5 43403.5 43404.5 43405.5 43406.5
[1405] 43407.5 43408.5 43409.5 43410.5 43411.5 43412.5 43413.5 43414.5 43415.5
[1414] 43416.5 43417.5 43418.5 43419.5 43420.5 43421.5 43422.5 43423.5 43424.5
[1423] 43425.5 43426.5 43427.5 43428.5 43429.5 43430.5 43431.5 43432.5 43433.5
[1432] 43434.5 43435.5 43436.5 43437.5 43438.5 43439.5 43440.5 43441.5 43442.5
[1441] 43443.5 43444.5 43445.5 43446.5 43447.5 43448.5 43449.5 43450.5 43451.5
[1450] 43452.5 43453.5 43454.5 43455.5 43456.5 43457.5 43458.5 43459.5 43460.5
[1459] 43461.5 43462.5 43463.5 43464.5 43465.5 43466.5 43467.5 43468.5 43469.5
[1468] 43470.5 43471.5 43472.5 43473.5 43474.5 43475.5 43476.5 43477.5 43478.5
[1477] 43479.5 43480.5 43481.5 43482.5 43483.5 43484.5 43485.5 43486.5 43487.5
[1486] 43488.5 43489.5 43490.5 43491.5 43492.5 43493.5 43494.5 43495.5 43496.5
[1495] 43497.5 43498.5 43499.5 43500.5 43501.5 43502.5 43503.5 43504.5 43505.5
[1504] 43506.5 43507.5 43508.5 43509.5 43510.5 43511.5 43512.5 43513.5 43514.5
[1513] 43515.5 43516.5 43517.5 43518.5 43519.5 43520.5 43521.5 43522.5 43523.5
[1522] 43524.5 43525.5 43526.5 43527.5 43528.5 43529.5 43530.5 43531.5 43532.5
[1531] 43533.5 43534.5 43535.5 43536.5 43537.5 43538.5 43539.5 43540.5 43541.5
[1540] 43542.5 43543.5 43544.5 43545.5 43546.5 43547.5 43548.5 43549.5 43550.5
[1549] 43551.5 43552.5 43553.5 43554.5 43555.5 43556.5 43557.5 43558.5 43559.5
[1558] 43560.5 43561.5 43562.5 43563.5 43564.5 43565.5 43566.5 43567.5 43568.5
[1567] 43569.5 43570.5 43571.5 43572.5 43573.5 43574.5 43575.5 43576.5 43577.5
[1576] 43578.5 43579.5 43580.5 43581.5 43582.5 43583.5 43584.5 43585.5 43586.5
[1585] 43587.5 43588.5 43589.5 43590.5 43591.5 43592.5 43593.5 43594.5 43595.5
[1594] 43596.5 43597.5 43598.5 43599.5 43600.5 43601.5 43602.5 43603.5 43604.5
[1603] 43605.5 43606.5 43607.5 43608.5 43609.5 43610.5 43611.5 43612.5 43613.5
[1612] 43614.5 43615.5 43616.5 43617.5 43618.5 43619.5 43620.5 43621.5 43622.5
[1621] 43623.5 43624.5 43625.5 43626.5 43627.5 43628.5 43629.5 43630.5 43631.5
[1630] 43632.5 43633.5 43634.5 43635.5 43636.5 43637.5 43638.5 43639.5 43640.5
[1639] 43641.5 43642.5 43643.5 43644.5 43645.5 43646.5 43647.5 43648.5 43649.5
[1648] 43650.5 43651.5 43652.5 43653.5 43654.5 43655.5 43656.5 43657.5 43658.5
[1657] 43659.5 43660.5 43661.5 43662.5 43663.5 43664.5 43665.5 43666.5 43667.5
[1666] 43668.5 43669.5 43670.5 43671.5 43672.5 43673.5 43674.5 43675.5 43676.5
[1675] 43677.5 43678.5 43679.5 43680.5 43681.5 43682.5 43683.5 43684.5 43685.5
[1684] 43686.5 43687.5 43688.5 43689.5 43690.5 43691.5 43692.5 43693.5 43694.5
[1693] 43695.5 43696.5 43697.5 43698.5 43699.5 43700.5 43701.5 43702.5 43703.5
[1702] 43704.5 43705.5 43706.5 43707.5 43708.5 43709.5 43710.5 43711.5 43712.5
[1711] 43713.5 43714.5 43715.5 43716.5 43717.5 43718.5 43719.5 43720.5 43721.5
[1720] 43722.5 43723.5 43724.5 43725.5 43726.5 43727.5 43728.5 43729.5 43730.5
[1729] 43731.5 43732.5 43733.5 43734.5 43735.5 43736.5 43737.5 43738.5 43739.5
[1738] 43740.5 43741.5 43742.5 43743.5 43744.5 43745.5 43746.5 43747.5 43748.5
[1747] 43749.5 43750.5 43751.5 43752.5 43753.5 43754.5 43755.5 43756.5 43757.5
[1756] 43758.5 43759.5 43760.5 43761.5 43762.5 43763.5 43764.5 43765.5 43766.5
[1765] 43767.5 43768.5 43769.5 43770.5 43771.5 43772.5 43773.5 43774.5 43775.5
[1774] 43776.5 43777.5 43778.5 43779.5 43780.5 43781.5 43782.5 43783.5 43784.5
[1783] 43785.5 43786.5 43787.5 43788.5 43789.5 43790.5 43791.5 43792.5 43793.5
[1792] 43794.5 43795.5 43796.5 43797.5 43798.5 43799.5 43800.5 43801.5 43802.5
[1801] 43803.5 43804.5 43805.5 43806.5 43807.5 43808.5 43809.5 43810.5 43811.5
[1810] 43812.5 43813.5 43814.5 43815.5 43816.5 43817.5 43818.5 43819.5 43820.5
[1819] 43821.5 43822.5 43823.5 43824.5 43825.5 43826.5 43827.5 43828.5 43829.5
[1828] 43830.5 43831.5 43832.5 43833.5 43834.5 43835.5 43836.5 43837.5 43838.5
[1837] 43839.5 43840.5 43841.5 43842.5 43843.5 43844.5 43845.5 43846.5 43847.5
[1846] 43848.5 43849.5 43850.5 43851.5 43852.5 43853.5 43854.5 43855.5 43856.5
[1855] 43857.5 43858.5 43859.5 43860.5 43861.5 43862.5 43863.5 43864.5 43865.5
[1864] 43866.5 43867.5 43868.5 43869.5 43870.5 43871.5 43872.5 43873.5 43874.5
[1873] 43875.5 43876.5 43877.5 43878.5 43879.5 43880.5 43881.5 43882.5 43883.5
[1882] 43884.5 43885.5 43886.5 43887.5 43888.5 43889.5 43890.5 43891.5 43892.5
[1891] 43893.5 43894.5 43895.5 43896.5 43897.5 43898.5 43899.5 43900.5 43901.5
[1900] 43902.5 43903.5 43904.5 43905.5 43906.5 43907.5 43908.5 43909.5 43910.5
[1909] 43911.5 43912.5 43913.5 43914.5 43915.5 43916.5 43917.5 43918.5 43919.5
[1918] 43920.5 43921.5 43922.5 43923.5 43924.5 43925.5 43926.5 43927.5 43928.5
[1927] 43929.5 43930.5 43931.5 43932.5 43933.5 43934.5 43935.5 43936.5 43937.5
[1936] 43938.5 43939.5 43940.5 43941.5 43942.5 43943.5 43944.5 43945.5 43946.5
[1945] 43947.5 43948.5 43949.5 43950.5 43951.5 43952.5 43953.5 43954.5 43955.5
[1954] 43956.5 43957.5 43958.5 43959.5 43960.5 43961.5 43962.5 43963.5 43964.5
[1963] 43965.5 43966.5 43967.5 43968.5 43969.5 43970.5 43971.5 43972.5 43973.5
[1972] 43974.5 43975.5 43976.5 43977.5 43978.5 43979.5 43980.5 43981.5 43982.5
[1981] 43983.5 43984.5 43985.5 43986.5 43987.5 43988.5 43989.5 43990.5 43991.5
[1990] 43992.5 43993.5 43994.5 43995.5 43996.5 43997.5 43998.5 43999.5 44000.5
[1999] 44001.5 44002.5 44003.5 44004.5 44005.5 44006.5 44007.5 44008.5 44009.5
[2008] 44010.5 44011.5 44012.5 44013.5 44014.5 44015.5 44016.5 44017.5 44018.5
[2017] 44019.5 44020.5 44021.5 44022.5 44023.5 44024.5 44025.5 44026.5 44027.5
[2026] 44028.5 44029.5 44030.5 44031.5 44032.5 44033.5 44034.5 44035.5 44036.5
[2035] 44037.5 44038.5 44039.5 44040.5 44041.5 44042.5 44043.5 44044.5 44045.5
[2044] 44046.5 44047.5 44048.5 44049.5 44050.5 44051.5 44052.5 44053.5 44054.5
[2053] 44055.5 44056.5 44057.5 44058.5 44059.5 44060.5 44061.5 44062.5 44063.5
[2062] 44064.5 44065.5 44066.5 44067.5 44068.5 44069.5 44070.5 44071.5 44072.5
[2071] 44073.5 44074.5 44075.5 44076.5 44077.5 44078.5 44079.5 44080.5 44081.5
[2080] 44082.5 44083.5 44084.5 44085.5 44086.5 44087.5 44088.5 44089.5 44090.5
[2089] 44091.5 44092.5 44093.5 44094.5 44095.5 44096.5 44097.5 44098.5 44099.5
[2098] 44100.5 44101.5 44102.5 44103.5 44104.5 44105.5 44106.5 44107.5 44108.5
[2107] 44109.5 44110.5 44111.5 44112.5 44113.5 44114.5 44115.5 44116.5 44117.5
[2116] 44118.5 44119.5 44120.5 44121.5 44122.5 44123.5 44124.5 44125.5 44126.5
[2125] 44127.5 44128.5 44129.5 44130.5 44131.5 44132.5 44133.5 44134.5 44135.5
[2134] 44136.5 44137.5 44138.5 44139.5 44140.5 44141.5 44142.5 44143.5 44144.5
[2143] 44145.5 44146.5 44147.5 44148.5 44149.5 44150.5 44151.5 44152.5 44153.5
[2152] 44154.5 44155.5 44156.5 44157.5 44158.5 44159.5 44160.5 44161.5 44162.5
[2161] 44163.5 44164.5 44165.5 44166.5 44167.5 44168.5 44169.5 44170.5 44171.5
[2170] 44172.5 44173.5 44174.5 44175.5 44176.5 44177.5 44178.5 44179.5 44180.5
[2179] 44181.5 44182.5 44183.5 44184.5 44185.5 44186.5 44187.5 44188.5 44189.5
[2188] 44190.5 44191.5 44192.5 44193.5 44194.5 44195.5 44196.5 44197.5 44198.5
[2197] 44199.5 44200.5 44201.5 44202.5 44203.5 44204.5 44205.5 44206.5 44207.5
[2206] 44208.5 44209.5 44210.5 44211.5 44212.5 44213.5 44214.5 44215.5 44216.5
[2215] 44217.5 44218.5 44219.5 44220.5 44221.5 44222.5 44223.5 44224.5 44225.5
[2224] 44226.5 44227.5 44228.5 44229.5 44230.5 44231.5 44232.5 44233.5 44234.5
[2233] 44235.5 44236.5 44237.5 44238.5 44239.5 44240.5 44241.5 44242.5 44243.5
[2242] 44244.5 44245.5 44246.5 44247.5 44248.5 44249.5 44250.5 44251.5 44252.5
[2251] 44253.5 44254.5 44255.5 44256.5 44257.5 44258.5 44259.5 44260.5 44261.5
[2260] 44262.5 44263.5 44264.5 44265.5 44266.5 44267.5 44268.5 44269.5 44270.5
[2269] 44271.5 44272.5 44273.5 44274.5 44275.5 44276.5 44277.5 44278.5 44279.5
[2278] 44280.5 44281.5 44282.5 44283.5 44284.5 44285.5 44286.5 44287.5 44288.5
[2287] 44289.5 44290.5 44291.5 44292.5 44293.5 44294.5 44295.5 44296.5 44297.5
[2296] 44298.5 44299.5 44300.5 44301.5 44302.5 44303.5 44304.5 44305.5 44306.5
[2305] 44307.5 44308.5 44309.5 44310.5 44311.5 44312.5 44313.5 44314.5 44315.5
[2314] 44316.5 44317.5 44318.5 44319.5 44320.5 44321.5 44322.5 44323.5 44324.5
[2323] 44325.5 44326.5 44327.5 44328.5 44329.5 44330.5 44331.5 44332.5 44333.5
[2332] 44334.5 44335.5 44336.5 44337.5 44338.5 44339.5 44340.5 44341.5 44342.5
[2341] 44343.5 44344.5 44345.5 44346.5 44347.5 44348.5 44349.5 44350.5 44351.5
[2350] 44352.5 44353.5 44354.5 44355.5 44356.5 44357.5 44358.5 44359.5 44360.5
[2359] 44361.5 44362.5 44363.5 44364.5 44365.5 44366.5 44367.5 44368.5 44369.5
[2368] 44370.5 44371.5 44372.5 44373.5 44374.5 44375.5 44376.5 44377.5 44378.5
[2377] 44379.5 44380.5 44381.5 44382.5 44383.5 44384.5 44385.5 44386.5 44387.5
[2386] 44388.5 44389.5 44390.5 44391.5 44392.5 44393.5 44394.5 44395.5 44396.5
[2395] 44397.5 44398.5 44399.5 44400.5 44401.5 44402.5 44403.5 44404.5 44405.5
[2404] 44406.5 44407.5 44408.5 44409.5 44410.5 44411.5 44412.5 44413.5 44414.5
[2413] 44415.5 44416.5 44417.5 44418.5 44419.5 44420.5 44421.5 44422.5 44423.5
[2422] 44424.5 44425.5 44426.5 44427.5 44428.5 44429.5 44430.5 44431.5 44432.5
[2431] 44433.5 44434.5 44435.5 44436.5 44437.5 44438.5 44439.5 44440.5 44441.5
[2440] 44442.5 44443.5 44444.5 44445.5 44446.5 44447.5 44448.5 44449.5 44450.5
[2449] 44451.5 44452.5 44453.5 44454.5 44455.5 44456.5 44457.5 44458.5 44459.5
[2458] 44460.5 44461.5 44462.5 44463.5 44464.5 44465.5 44466.5 44467.5 44468.5
[2467] 44469.5 44470.5 44471.5 44472.5 44473.5 44474.5 44475.5 44476.5 44477.5
[2476] 44478.5 44479.5 44480.5 44481.5 44482.5 44483.5 44484.5 44485.5 44486.5
[2485] 44487.5 44488.5 44489.5 44490.5 44491.5 44492.5 44493.5 44494.5 44495.5
[2494] 44496.5 44497.5 44498.5 44499.5 44500.5 44501.5 44502.5 44503.5 44504.5
[2503] 44505.5 44506.5 44507.5 44508.5 44509.5 44510.5 44511.5 44512.5 44513.5
[2512] 44514.5 44515.5 44516.5 44517.5 44518.5 44519.5 44520.5 44521.5 44522.5
[2521] 44523.5 44524.5 44525.5 44526.5 44527.5 44528.5 44529.5 44530.5 44531.5
[2530] 44532.5 44533.5 44534.5 44535.5 44536.5 44537.5 44538.5 44539.5 44540.5
[2539] 44541.5 44542.5 44543.5 44544.5 44545.5 44546.5 44547.5 44548.5 44549.5
[2548] 44550.5 44551.5 44552.5 44553.5 44554.5 44555.5 44556.5 44557.5 44558.5
[2557] 44559.5tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt[1] 2557tunits$hasatt
[1] TRUE
$value
[1] "days since 1900-01-01 00:00:00"# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)[1] 336 289 2557# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
dim(oxy_array)[1] 336 289 2557str(dim(oxy_array)) int [1:3] 336 289 2557# The third slot is the date index
# Loop through all "dates", put into a list
dlist <- list()
for(i in 1:length(months)) {
oxy_sub <- oxy_array[, , i]
dlist[[i]] <- oxy_sub
}
# Name the list
names(dlist) <- paste(months, years, sep = "_")
str(dlist)List of 2557
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 1_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 2_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 3_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
$ 4_2015 : num [1:336, 1:289] NA NA NA NA NA NA NA NA NA NA ...
[list output truncated]# Create data holding object
oxy_data_list <- list()
# Loop through each month_year and extract raster values for the cpue data points
for(i in unique(dat_1$month_year)) {
# Set plot limits
ymin = 54; ymax = 58; xmin = 12; xmax = 22
# Subset a month-year combination
oxy_slice <- dlist[[i]]
# Create raster for that year (i)
r <- raster(t(oxy_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
plot(r, main = paste(i), ylim = c(ymin, ymax), xlim = c(xmin, xmax))
# Filter the same year (i) in the cpue data and select only coordinates
d_slice <- dat_1 %>% filter(month_year == i) %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
data_sp <- SpatialPoints(d_slice)
# Extract raster value (oxygen)
rasValue <- raster::extract(r, data_sp)
# Now we want to plot the results of the raster extractions by plotting the cpue data points over a raster and saving it for each year.
# Make the SpatialPoints object into a raster again (for plot)
df <- as.data.frame(data_sp)
# Add in the raster value in the df holding the coordinates for the cpue data
d_slice$oxy <- rasValue
# Add in which year
d_slice$month_year <- i
# Now the unit of oxygen is mmol/m3. I want it to be ml/L. The original model is in unit ml/L
# and it's been converted by the data host. Since it was converted without accounting for
# pressure or temperature, I can simply use the following conversion factor:
# 1 ml/l = 103/22.391 = 44.661 μmol/l -> 1 ml/l = 0.044661 mmol/l = 44.661 mmol/m^3 -> 0.0223909 ml/l = 1mmol/m^3
# https://ocean.ices.dk/tools/unitconversion.aspx
d_slice$oxy <- d_slice$oxy * 0.0223909
# Add each years' data in the list
oxy_data_list[[i]] <- d_slice
}filter: removed 289 rows (95%), 16 rows remaining
filter: removed 278 rows (91%), 27 rows remaining
filter: removed 302 rows (99%), 3 rows remaining
filter: removed 286 rows (94%), 19 rows remaining
filter: removed 290 rows (95%), 15 rows remaining
filter: removed 284 rows (93%), 21 rows remaining
filter: removed 288 rows (94%), 17 rows remaining
filter: removed 289 rows (95%), 16 rows remaining
filter: removed 290 rows (95%), 15 rows remaining
filter: removed 299 rows (98%), 6 rows remaining
filter: removed 280 rows (92%), 25 rows remaining
filter: removed 289 rows (95%), 16 rows remaining
filter: removed 281 rows (92%), 24 rows remaining
filter: removed 288 rows (94%), 17 rows remaining
filter: removed 272 rows (89%), 33 rows remaining
filter: removed 297 rows (97%), 8 rows remaining
filter: removed 278 rows (91%), 27 rows remaining
# Now create a data frame from the list of all annual values
big_dat_oxy <- dplyr::bind_rows(oxy_data_list)
sort(unique(big_dat_oxy$month_year)) [1] "11_2015" "11_2016" "11_2017" "11_2018" "11_2019" "11_2020" "11_2021"
[8] "2_2016" "2_2017" "2_2018" "2_2020" "2_2021" "3_2016" "3_2017"
[15] "3_2018" "3_2020" "3_2021" New oxygen
# 2022 Q1
# Note this is from the projection, not the re-analysis because it hasn't been updated
ncin <- nc_open(paste0(home, "/data/NEMO/BAL-ERGOM_BGC-MonthlyMeans-202202.nc"))
print(ncin)File /Users/maxlindmark/Dropbox/Max work/R/cod-interactions/data/NEMO/BAL-ERGOM_BGC-MonthlyMeans-202202.nc (NC_FORMAT_NETCDF4_CLASSIC):
13 variables (excluding dimension variables):
float chl[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mg m-3
long_name: Chlorophyll_a_Concentration
standard_name: mass_concentration_of_chlorophyll_a_in_sea_water
unit_long: mass per unit volume
missing_value: -999
float phyc[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Phytoplankton Concentration
standard_name: mole_concentration_of_phytoplankton_expressed_as_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float o2b[lon,lat,time] (Chunking: [763,774,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Dissolved_Oxygen_Concentration
standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
unit_long: millimole O2 per unit volume
missing_value: -999
float no3[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Nitrate_Concentration
standard_name: mole_concentration_of_nitrate_in_sea_water
unit_long: millimole N per unit volume
missing_value: -999
float kd[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: 1/m
long_name: Light Attenuation Coefficient
standard_name: volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water
unit_long: 1/m
missing_value: -999
float o2[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Dissolved_Oxygen_Concentration
standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
unit_long: millimole O2 per unit volume
missing_value: -999
float pH[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: 1
long_name: sea_water_ph
standard_name: sea_water_ph_reported_on_total_scale
unit_long: 1
missing_value: -999
float po4[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Phosphate_Concentration
standard_name: mole_concentration_of_phosphate_in_sea_water
unit_long: millimole P per unit volume
missing_value: -999
float zooc[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Zooplankton Concentration
standard_name: mole_concentration_of_zooplankton_expressed_as_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float spCO2[lon,lat,time] (Chunking: [763,774,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: Pa
long_name: surface_partial_pressure_of_carbon_dioxide
standard_name: surface_partial_pressure_of_carbon_dioxide_in_sea_water
unit_long: pascal
missing_value: -999
float nh4[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Ammonium_Concentration
standard_name: mole_concentration_of_ammonium_in_sea_water
unit_long: millimole N per unit volume
missing_value: -999
float dissic[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mol m-3
long_name: Dissolved_Inorganic_Carbon_Concentration
standard_name: mole_concentration_of_dissolved_inorganic_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float si[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Silica_Concentration
standard_name: mole_concentration_of_silicate_in_sea_water
unit_long: millimole Silicate per unit volume
missing_value: -999
4 dimensions:
time Size:1 *** is unlimited ***
units: days since 1900-01-01 00:00:00
long_name: time
standard_name: time
axis: T
depth Size:56
units: m
comment:
unit_long: meters
reference: sea_level SeaDataNet L111
positive: down
uncertainty:
valid_min: 0
QC_procedure: 1
long_name: Depth of each measurement
standard_name: depth
QC_indicator: 1
valid_max: 12000
axis: Z
lon Size:763
units: degrees_east
long_name: Longitude of each location
standard_name: longitude
unit_long: degrees East
axis: X
lat Size:774
units: degrees_north
long_name: Latitude of each location
standard_name: latitude
unit_long: degrees North
axis: Y
18 global attributes:
comment: Data on cropped native product grid. Horizontal velocities destagged
grid_resolution: ~1 nautical mile (1min latitude; 1min40sec longitude)
title: CMEMS ERGOM monthly integrated model fields
Conventions: CF-1.0
source: CMEMS BAL MFC NEMO model output converted to NetCDF
contact: servicedesk.cmems@mercator-ocean.eu
references: https://marine.copernicus.eu/
file_quality_index: 1
institution: Baltic MFC, PU Swedish Meteorological and Hydrological Institute
compression: yes
stop_date: 2022-02-15 03:29:27
creation_date: 2023-09-30 01:48:04
netcdf_version_id: 4.4.1.1 of Oct 24 2018 14:14:02 $
easternmost_longitude: 30.2086563110352
northernmost_latitude: 65.8909912109375
westernmost_longitude: 9.04158210754395
southernmost_latitude: 53.0082969665527
start_date: 2022-02-15 03:29:27# Get longitude and latitude
lon <- ncvar_get(ncin,"lon")
nlon <- dim(lon)
head(lon)[1] 9.041582 9.069360 9.097137 9.124915 9.152693 9.180470lat <- ncvar_get(ncin,"lat")
nlat <- dim(lat)
head(lat)[1] 53.00830 53.02496 53.04163 53.05830 53.07496 53.09163# Get time
time <- ncvar_get(ncin,"time")
time[1] 44605.15tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt[1] 1tunits$hasatt
[1] TRUE
$value
[1] "days since 1900-01-01 00:00:00"# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)[1] 763 774# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
# Create raster for that year (i)
r <- raster(t(oxy_array), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
plot(r, main = paste("2022_q1"), ylim = c(ymin, ymax), xlim = c(xmin, xmax))
# Filter the same year (i) in the cpue data and select only coordinates
dat_2_q1 <- dat_2 %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
dat_2_q1_sp <- SpatialPoints(dat_2_q1)
# Extract raster value (oxygen)
rasValue <- raster::extract(r, dat_2_q1_sp)
dat_2_q1$oxy <- rasValue
# Add in which year
dat_2_q1$month_year <- "2_2022"
head(dat_2_q1)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.4 55.7 307. 2_2022
2 14.4 55.7 287. 2_2022
3 14.7 55.7 282. 2_2022
4 15.9 55.8 242. 2_2022
5 13.7 55.2 361. 2_2022
6 14.7 55.6 240. 2_2022 dat_2_q1$oxy <- dat_2_q1$oxy * 0.0223909
hist(dat_2_q1$oxy)
# Now do Q4
ncin <- nc_open(paste0(home, "/data/NEMO/BAL-ERGOM_BGC-MonthlyMeans-202211.nc"))
print(ncin)File /Users/maxlindmark/Dropbox/Max work/R/cod-interactions/data/NEMO/BAL-ERGOM_BGC-MonthlyMeans-202211.nc (NC_FORMAT_NETCDF4_CLASSIC):
13 variables (excluding dimension variables):
float chl[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mg m-3
long_name: Chlorophyll_a_Concentration
standard_name: mass_concentration_of_chlorophyll_a_in_sea_water
unit_long: mass per unit volume
missing_value: -999
float phyc[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Phytoplankton Concentration
standard_name: mole_concentration_of_phytoplankton_expressed_as_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float o2b[lon,lat,time] (Chunking: [763,774,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Dissolved_Oxygen_Concentration
standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
unit_long: millimole O2 per unit volume
missing_value: -999
float no3[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Nitrate_Concentration
standard_name: mole_concentration_of_nitrate_in_sea_water
unit_long: millimole N per unit volume
missing_value: -999
float kd[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: 1/m
long_name: Light Attenuation Coefficient
standard_name: volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water
unit_long: 1/m
missing_value: -999
float o2[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Dissolved_Oxygen_Concentration
standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
unit_long: millimole O2 per unit volume
missing_value: -999
float pH[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: 1
long_name: sea_water_ph
standard_name: sea_water_ph_reported_on_total_scale
unit_long: 1
missing_value: -999
float po4[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Phosphate_Concentration
standard_name: mole_concentration_of_phosphate_in_sea_water
unit_long: millimole P per unit volume
missing_value: -999
float zooc[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Zooplankton Concentration
standard_name: mole_concentration_of_zooplankton_expressed_as_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float spCO2[lon,lat,time] (Chunking: [763,774,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: Pa
long_name: surface_partial_pressure_of_carbon_dioxide
standard_name: surface_partial_pressure_of_carbon_dioxide_in_sea_water
unit_long: pascal
missing_value: -999
float nh4[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Ammonium_Concentration
standard_name: mole_concentration_of_ammonium_in_sea_water
unit_long: millimole N per unit volume
missing_value: -999
float dissic[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mol m-3
long_name: Dissolved_Inorganic_Carbon_Concentration
standard_name: mole_concentration_of_dissolved_inorganic_carbon_in_sea_water
unit_long: millimole C per unit volume
missing_value: -999
float si[lon,lat,depth,time] (Chunking: [191,194,14,1]) (Compression: shuffle,level 1)
_FillValue: -999
units: mmol m-3
long_name: Silica_Concentration
standard_name: mole_concentration_of_silicate_in_sea_water
unit_long: millimole Silicate per unit volume
missing_value: -999
4 dimensions:
time Size:1 *** is unlimited ***
units: days since 1900-01-01 00:00:00
long_name: time
standard_name: time
axis: T
depth Size:56
units: m
comment:
unit_long: meters
reference: sea_level SeaDataNet L111
positive: down
uncertainty:
valid_min: 0
QC_procedure: 1
long_name: Depth of each measurement
standard_name: depth
QC_indicator: 1
valid_max: 12000
axis: Z
lon Size:763
units: degrees_east
long_name: Longitude of each location
standard_name: longitude
unit_long: degrees East
axis: X
lat Size:774
units: degrees_north
long_name: Latitude of each location
standard_name: latitude
unit_long: degrees North
axis: Y
18 global attributes:
comment: Data on cropped native product grid. Horizontal velocities destagged
grid_resolution: ~1 nautical mile (1min latitude; 1min40sec longitude)
title: CMEMS ERGOM monthly integrated model fields
Conventions: CF-1.0
source: CMEMS BAL MFC NEMO model output converted to NetCDF
contact: servicedesk.cmems@mercator-ocean.eu
references: https://marine.copernicus.eu/
file_quality_index: 1
institution: Baltic MFC, PU Swedish Meteorological and Hydrological Institute
compression: yes
stop_date: 2022-11-27 06:00:00
creation_date: 2023-11-08 23:27:07
netcdf_version_id: 4.4.1.1 of Oct 24 2018 14:14:02 $
easternmost_longitude: 30.2086563110352
northernmost_latitude: 65.8909912109375
westernmost_longitude: 9.04158210754395
southernmost_latitude: 53.0082969665527
start_date: 2022-11-27 06:00:00# Get longitude and latitude
lon <- ncvar_get(ncin,"lon")
nlon <- dim(lon)
head(lon)[1] 9.041582 9.069360 9.097137 9.124915 9.152693 9.180470lat <- ncvar_get(ncin,"lat")
nlat <- dim(lat)
head(lat)[1] 53.00830 53.02496 53.04163 53.05830 53.07496 53.09163# Get time
time <- ncvar_get(ncin,"time")
time[1] 44890.25tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt[1] 1tunits$hasatt
[1] TRUE
$value
[1] "days since 1900-01-01 00:00:00"# Get oxygen
dname <- "o2b"
oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)[1] 763 774# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")
# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])
# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))
# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)
# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA
# Create raster for that year (i)
r <- raster(t(oxy_array), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
# Flip...
r <- flip(r, direction = 'y')
plot(r, main = paste("2022_q4"), ylim = c(ymin, ymax), xlim = c(xmin, xmax))
# Filter the same year (i) in the cpue data and select only coordinates
dat_2_q4 <- dat_2 %>% dplyr::select(lon, lat)
# Make into a SpatialPoints object
dat_2_q4_sp <- SpatialPoints(dat_2_q4)
# Extract raster value (oxygen)
rasValue <- raster::extract(r, dat_2_q4_sp)
dat_2_q4$oxy <- rasValue
# Add in which year
dat_2_q4$month_year <- "11_2022"
head(dat_2_q4)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.4 55.7 328. 11_2022
2 14.4 55.7 324. 11_2022
3 14.7 55.7 207. 11_2022
4 15.9 55.8 167. 11_2022
5 13.7 55.2 258. 11_2022
6 14.7 55.6 161. 11_2022 dat_2_q4$oxy <- dat_2_q4$oxy * 0.0223909
hist(dat_2_q4$oxy)
New oxygen
head(dat_2_q1)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.4 55.7 6.88 2_2022
2 14.4 55.7 6.43 2_2022
3 14.7 55.7 6.31 2_2022
4 15.9 55.8 5.42 2_2022
5 13.7 55.2 8.08 2_2022
6 14.7 55.6 5.36 2_2022 head(dat_2_q4)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.4 55.7 7.35 11_2022
2 14.4 55.7 7.25 11_2022
3 14.7 55.7 4.64 11_2022
4 15.9 55.8 3.74 11_2022
5 13.7 55.2 5.78 11_2022
6 14.7 55.6 3.60 11_2022 hist(dat_2_q1$oxy)
hist(dat_2_q4$oxy)
Join with stomach data
d_wide4 <- d_wide3
d_wide4 <- d_wide4 %>%
mutate(month_2 = ifelse(quarter == 1, 2, 11), # most common months
month_year = paste(month_2, year, sep = "_")) %>%
dplyr::select(-month_2)mutate: new variable 'month_2' (double) with 2 unique values and 0% NA new variable 'month_year' (character) with 14 unique values and 0% NAunique(d_wide4$month_year) [1] "11_2015" "2_2016" "11_2016" "2_2017" "11_2017" "2_2018" "11_2018"
[8] "11_2019" "2_2020" "11_2020" "2_2021" "11_2021" "2_2022" "11_2022"unique(big_dat_oxy$month_year) [1] "11_2015" "2_2016" "3_2016" "11_2016" "2_2017" "3_2017" "11_2017"
[8] "2_2018" "3_2018" "11_2018" "11_2019" "2_2020" "3_2020" "11_2020"
[15] "2_2021" "3_2021" "11_2021"# Add saduria
d_wide4 <- left_join(d_wide4, dat %>% dplyr::select(X, Y, density_saduria) %>% distinct(X, Y, .keep_all = TRUE))distinct: removed 137 rows (39%), 217 rows remainingJoining with `by = join_by(X, Y)`
left_join: added one column (density_saduria)
> rows only in x 0
> rows only in y ( 0)
> matched rows 9,649
> =======
> rows total 9,649# d_wide5 <- left_join(d_wide4, dat %>% dplyr::select(X, Y, density_saduria)) #%>% distinct(X, Y, .keep_all = TRUE))
#
# dat2 <- dat %>% dplyr::select(X, Y, density_saduria)
#
# dat2 %>%
# group_by(X, Y) %>%
# summarise(n = n()) %>%
# ungroup() %>%
# distinct(n)
#
#
# d_wide4 %>%
# group_by(pred_id) %>%
# summarise(n = n()) %>%
# distinct(n)
#
# d_wide5 %>%
# group_by(pred_id) %>%
# summarise(n = n()) %>%
# distinct(n)
#
# d_wide5 %>%
# group_by(pred_id) %>%
# mutate(n = n()) %>%
# filter(pred_id == "2015_4_COD_169") %>%
# as.data.frame()
# Add oxygen (bind_rows first)
head(big_dat_oxy)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.6 55.6 3.97 11_2015
2 14.5 55.7 5.44 11_2015
3 14.4 55.7 6.25 11_2015
4 17.4 56.1 5.59 11_2015
5 18.4 56.3 7.01 11_2015
6 18.6 56.4 4.83 11_2015 head(dat_2_q1)# A tibble: 6 × 4
lon lat oxy month_year
<dbl> <dbl> <dbl> <chr>
1 14.4 55.7 6.88 2_2022
2 14.4 55.7 6.43 2_2022
3 14.7 55.7 6.31 2_2022
4 15.9 55.8 5.42 2_2022
5 13.7 55.2 8.08 2_2022
6 14.7 55.6 5.36 2_2022 oxy_all <- bind_rows(big_dat_oxy, dat_2_q1, dat_2_q4)
ggplot(oxy_all, aes(lon, lat, color = oxy)) +
geom_point() +
facet_wrap(~month_year)
d_wide4 <- d_wide4 %>% left_join(oxy_all, by = c("lon", "lat", "month_year"))left_join: added one column (oxy)
> rows only in x 1,790
> rows only in y ( 114)
> matched rows 8,188 (includes duplicates)
> =======
> rows total 9,978ggplot(d_wide4, aes(lon, lat, color = oxy)) +
geom_point() +
facet_wrap(~month_year)
d_wide4 <- d_wide4 %>% dplyr::select(-month_year)Save
write_csv(d_wide4, paste0(home, "/data/clean/aggregated_stomach_data.csv"))